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PD-GATv2: positive difference second generation graph attention network based on multi-granularity in information systems to classification

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Abstract

The Graph Attention Network (GAT) is a widely recognized architecture in the field of Graph Neural Networks (GNNs). It is considered the state-of-the-art approach for graph representation learning. In recent years, several researchers have successfully applied GAT to structured Euclidean data, including images and languages. Additionally, the Graph Convolutional Network (GCN) has also been adopted in information systems, such as PN-GCN, which establishes undirected graphs for classification. However, compared to undirected graphs, directed graphs contain directional information that more comprehensively describes the relations between objects in the graph. Therefore, we establish a directed graph in the information system and further analyze it using GAT. As a first step, this article introduces the concept of multi-granularity object directed weighted graphs. Then, we construct the second generation of the residual edge-weighted graph attention neural network model (PD-GATv2) based on these directed graphs. Finally, we verify the effectiveness and generalizability of the PD-GATv2 algorithm through experiments, and its effectiveness is further demonstrated through ablation experiments.

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Acknowledgements

This work is supported by Hunan Provincial Natural Science Foundation of China (2023JJ30387) and Scientific Research Fund of Hunan Provincial Education Department of China (23B0072).

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Authors and Affiliations

  1. College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, 410081, China

    Yu Fu, Xindi Liu & Bin Yu

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  1. Yu Fu
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  2. Xindi Liu
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  3. Bin Yu
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Contributions

Bin Yu: Conceptualization, Methodology, Supervision, Writing - original draft, Writing - review & editing. Xindi Liu: Conceptualization, Methodology, Supervision, Writing - review & editing. Yu Fu: Conceptualization, Methodology, Supervision, Writing - review & editing.

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Correspondence to Bin Yu.

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The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Fu, Y., Liu, X. & Yu, B. PD-GATv2: positive difference second generation graph attention network based on multi-granularity in information systems to classification. Appl Intell 54, 5081–5096 (2024). https://doi.org/10.1007/s10489-024-05432-y

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